A Robust Real Time Bidding Strategy Against Inaccurate CTR Predictions by Using Cluster Expected Win Rate

Wen Yueh Shih, Hsu Chao Lai, Jiun Long Huang*


研究成果: Article同行評審


Previous real-time bidding (RTB) strategies offer a bidding price for each incoming bid request based on its individual predicted click through rate (CTR). However, this pricing mechanism could be a pitfall because the large and sparse feature space often leads to inaccurate individual CTR predictions. Furthermore, our observations in a real-world online advertising environment indicate that the predicted CTR could be uncorrelated to the empirical CTR. In this paper, we introduce a new evaluation metric, cluster expected win rate (CEWR), and propose a novel framework Cluster-aware Ranking-based Bidding Strategy (CARBS) that leverages CEWR to cope with the above issue. CEWR quantifies the worthiness of each bid request based on a group of bid requests having similar expected performance. First, a two-step clustering method aggregates bid requests with similar predicted CTRs into clusters to gather similar information. Second, CARBS ranks the clusters and sets the Affordability Threshold in order to spend budgets smartly. CEWR summarizes the above results and hence better correlates to the click performance in our observations, causing the robustness superior to the inaccurate individual CTR predictions. Finally, a reinforcement learning-based bidding strategy is conducted to adjust the bid request expected win rate (BEWR) jointly based on CEWR and the dynamic market for deriving the final bid prices. The experimental results on three real ad campaigns manifest that CARBS outperforms state-of-the-art bidding strategies in terms of click acquisition. In a poorly predicted campaign (AUC: 0.73) with an extremely tight budget, the improvement is 32.5%, showing the robustness of CARBS.

頁(從 - 到)126917-126926
期刊IEEE Access
出版狀態Published - 2023


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